# Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation

arXiv: https://arxiv.org/abs/1705.00389 · HTML: https://mlsanity.com/paper/1705.00389/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2017.

## Built on by (most cited first, 30 of 35)

- [Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression](https://mlsanity.com/paper/2104.02300/) (2021)
- [Including Signed Languages in Natural Language Processing](https://mlsanity.com/paper/2105.05222/) (2021)
- [Online Knowledge Distillation for Efficient Pose Estimation](https://mlsanity.com/paper/2108.02092/) (2021)
- [Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective](https://mlsanity.com/paper/2104.11536/) (2021)
- [Human Pose as Compositional Tokens](https://mlsanity.com/paper/2303.11638/) (2023)
- [Zoo-Tuning: Adaptive Transfer from a Zoo of Models](https://mlsanity.com/paper/2106.15434/) (2021)
- [Regressive Domain Adaptation for Unsupervised Keypoint Detection](https://mlsanity.com/paper/2103.06175/) (2021)
- [Evaluating the Immediate Applicability of Pose Estimation for Sign Language Recognition](https://mlsanity.com/paper/2104.10166/) (2021)
- [CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution](https://mlsanity.com/paper/2204.08696/) (2022)
- [PoseTrans: A Simple Yet Effective Pose Transformation Augmentation for Human Pose Estimation](https://mlsanity.com/paper/2208.07755/) (2022)
- [SHaRPose: Sparse High-Resolution Representation for Human Pose Estimation](https://mlsanity.com/paper/2312.10758/) (2023)
- [On Triangulation as a Form of Self-Supervision for 3D Human Pose Estimation](https://mlsanity.com/paper/2203.15865/) (2022)
- [An Adversarial Human Pose Estimation Network Injected with Graph Structure](https://mlsanity.com/paper/2103.15534/) (2021)
- [HumanVLM: Foundation for Human-Scene Vision-Language Model](https://mlsanity.com/paper/2411.03034/) (2024)
- [Learn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images](https://mlsanity.com/paper/2105.09124/) (2021)
- [Human Pose-based Estimation, Tracking and Action Recognition with Deep Learning: A Survey](https://mlsanity.com/paper/2310.13039/) (2023)
- [Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models](https://mlsanity.com/paper/2206.03726/) (2022)
- [Single Person Pose Estimation: A Survey](https://mlsanity.com/paper/2109.10056/) (2021)
- [Estimating Pose from Pressure Data for Smart Beds with Deep Image-based Pose Estimators](https://mlsanity.com/paper/2206.06518/) (2022)
- [Animal Behavior Analysis Methods Using Deep Learning: A Survey](https://mlsanity.com/paper/2405.14002/) (2024)
- [Human Modelling and Pose Estimation Overview](https://mlsanity.com/paper/2406.19290/) (2024)
- [Bias-Compensated Integral Regression for Human Pose Estimation](https://mlsanity.com/paper/2301.10431/) (2023)
- [Small but Mighty: Enhancing 3D Point Clouds Semantic Segmentation with U-Next Framework](https://mlsanity.com/paper/2304.00749/) (2023)
- [LDRNet: Enabling Real-time Document Localization on Mobile Devices](https://mlsanity.com/paper/2206.02136/) (2022)
- [Multimodal Active Measurement for Human Mesh Recovery in Close Proximity](https://mlsanity.com/paper/2310.08116/) (2023)
- [Toward Real World Stereo Image Super-Resolution via Hybrid Degradation Model and Discriminator for Implied Stereo Image Information](https://mlsanity.com/paper/2312.07934/) (2023)
- [Open-VICO: An Open-Source Gazebo Toolkit for Vision-based Skeleton Tracking in Human-Robot Collaboration](https://mlsanity.com/paper/2203.14733/) (2022)
- [PoseAction: Action Recognition for Patients in the Ward using Deep Learning Approaches](https://mlsanity.com/paper/2310.03288/) (2023)
- [PoseSyn: Synthesizing Diverse 3D Pose Data from In-the-Wild 2D Data](https://mlsanity.com/paper/2503.13025/) (2025)
- [Self-Supervision and Spatial-Sequential Attention Based Loss for Multi-Person Pose Estimation](https://mlsanity.com/paper/2110.10734/) (2021)

## Abstract (arXiv)

For human pose estimation in monocular images, joint occlusions and overlapping upon human bodies often result in deviated pose predictions. Under these circumstances, biologically implausible pose predictions may be produced. In contrast, human vision is able to predict poses by exploiting geometric constraints of joint inter-connectivity. To address the problem by incorporating priors about the structure of human bodies, we propose a novel structure-aware convolutional network to implicitly take such priors into account during training of the deep network. Explicit learning of such constraints is typically challenging. Instead, we design discriminators to distinguish the real poses from the fake ones (such as biologically implausible ones). If the pose generator (G) generates results that the discriminator fails to distinguish from real ones, the network successfully learns the priors.